library(tidyverse)
library(lubridate)Lab 7
On the Computer
Loading data from a github repository
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "data/time_series_covid19_confirmed_global.csv")time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")Data Tidying - Pivoting
time_series_confirmed_long <- time_series_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed") Dates and time
time_series_confirmed_long$Date <- mdy(time_series_confirmed_long$Date)Making Graphs from the time series data
time_series_confirmed_long|>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Confirmed Cases")
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily <-time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_line() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth() +
ggtitle("COVID-19 Confirmed Cases")
time_series_confirmed_long_daily |>
filter (Country_Region == "US") |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("COVID-19 Confirmed Cases")
Animated Graphs with gganimate
Installing gganimate and gifski
library(gganimate)
library(gifski)
theme_set(theme_bw())An animation of the confirmed cases in select countries
daily_counts <- time_series_confirmed_long_daily |>
filter (Country_Region == "US")
p <- ggplot(daily_counts, aes(x = Date, y = Daily, color = Country_Region)) +
geom_point() +
ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
anim_save("daily_counts_US.gif", p)Animation of confirmed deaths
# This download may take about 5 minutes. You only need to do this once so set `#| eval: false` in your qmd file
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data/time_series_covid19_deaths_global.csv")time_series_deaths_confirmed <- read_csv("data/time_series_covid19_deaths_global.csv")|>
rename(Province_State = "Province/State", Country_Region = "Country/Region")
time_series_deaths_long <- time_series_deaths_confirmed |>
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)p <- time_series_deaths_long |>
filter (Country_Region %in% c("US","Canada", "Mexico","Brazil","Egypt","Ecuador","India", "Netherlands", "Germany", "China" )) |>
ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) +
geom_point(aes(size=Confirmed)) +
transition_time(Date) +
labs(title = "Cumulative Deaths: {frame_time}") +
ylab("Deaths") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)
Exercises
Exercise 1 Go through Chapter 5 in R for Data Sciences - Data Tiyding and Pivot](https://r4ds.hadley.nz/data-tidy.html) putting the examples and exerices into your report as in Lab 2 and 3
5 Data tidying
5.1 Introduction
5.1.1 Prerequisites
library(tidyverse)5.2 Tidy data
table1# A tibble: 6 × 4
country year cases population
<chr> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071
2 Afghanistan 2000 2666 20595360
3 Brazil 1999 37737 172006362
4 Brazil 2000 80488 174504898
5 China 1999 212258 1272915272
6 China 2000 213766 1280428583table2# A tibble: 12 × 4
country year type count
<chr> <dbl> <chr> <dbl>
1 Afghanistan 1999 cases 745
2 Afghanistan 1999 population 19987071
3 Afghanistan 2000 cases 2666
4 Afghanistan 2000 population 20595360
5 Brazil 1999 cases 37737
6 Brazil 1999 population 172006362
7 Brazil 2000 cases 80488
8 Brazil 2000 population 174504898
9 China 1999 cases 212258
10 China 1999 population 1272915272
11 China 2000 cases 213766
12 China 2000 population 1280428583table3# A tibble: 6 × 3
country year rate
<chr> <dbl> <chr>
1 Afghanistan 1999 745/19987071
2 Afghanistan 2000 2666/20595360
3 Brazil 1999 37737/172006362
4 Brazil 2000 80488/174504898
5 China 1999 212258/1272915272
6 China 2000 213766/1280428583# Compute rate per 10,000
table1 |>
mutate(rate = cases / population * 10000)# A tibble: 6 × 5
country year cases population rate
<chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071 0.373
2 Afghanistan 2000 2666 20595360 1.29
3 Brazil 1999 37737 172006362 2.19
4 Brazil 2000 80488 174504898 4.61
5 China 1999 212258 1272915272 1.67
6 China 2000 213766 1280428583 1.67 # Compute total cases per year
table1 |>
group_by(year) |>
summarize(total_cases = sum(cases))# A tibble: 2 × 2
year total_cases
<dbl> <dbl>
1 1999 250740
2 2000 296920# Visualize changes over time
ggplot(table1, aes(x = year, y = cases)) +
geom_line(aes(group = country), color = "grey50") +
geom_point(aes(color = country, shape = country)) +
scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000
5.2.1 Exercises
1. For each of the sample tables, describe what each observation and each column represents.
# The observation is a single country–year pair. The columns have the country being observed, the year of observation, the number of reported cases in that year, and the total population of the country in that year. Each row gives two variables (cases and population) for the same unit (country–year).
table1# A tibble: 6 × 4
country year cases population
<chr> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071
2 Afghanistan 2000 2666 20595360
3 Brazil 1999 37737 172006362
4 Brazil 2000 80488 174504898
5 China 1999 212258 1272915272
6 China 2000 213766 1280428583# The observation is a single measurement (either cases or population) for a given country–year. The columns have the country being observed, the year of observation, the kind of measurement ("cases" or "population"), and the numeric value of that measurement. Each row is one variable’s value for a country–year.
table2# A tibble: 12 × 4
country year type count
<chr> <dbl> <chr> <dbl>
1 Afghanistan 1999 cases 745
2 Afghanistan 1999 population 19987071
3 Afghanistan 2000 cases 2666
4 Afghanistan 2000 population 20595360
5 Brazil 1999 cases 37737
6 Brazil 1999 population 172006362
7 Brazil 2000 cases 80488
8 Brazil 2000 population 174504898
9 China 1999 cases 212258
10 China 1999 population 1272915272
11 China 2000 cases 213766
12 China 2000 population 1280428583# The observation is a single country–year pair. The columns have the country being observed, the year of observation, and the ratio of cases to population, stored as a string. Each row gives a derived variable (rate) for a country–year, but the rate is not yet numeric — it’s stored as text representing a fraction.
table3# A tibble: 6 × 3
country year rate
<chr> <dbl> <chr>
1 Afghanistan 1999 745/19987071
2 Afghanistan 2000 2666/20595360
3 Brazil 1999 37737/172006362
4 Brazil 2000 80488/174504898
5 China 1999 212258/1272915272
6 China 2000 213766/12804285832. Sketch out the process you’d use to calculate the rate for table2 and table3. You will need to perform four operations:
a. Extract the number of TB cases per country per year.
# table2
library(dplyr)
cases_tbl2 <- table2 %>%
filter(type == "cases") %>%
select(country, year, cases = count)
# table3
library(tidyr)
parsed_tbl3 <- table3 %>%
separate(rate, into = c("cases", "population"), sep = "/", convert = TRUE)
cases_tbl3 <- parsed_tbl3 %>%
select(country, year, cases)b. Extract the matching population per country per year.
# table2
population_tbl2 <- table2 %>%
filter(type == "population") %>%
select(country, year, population = count)
# table3
population_tbl3 <- parsed_tbl3 %>%
select(country, year, population)c. Divide cases by population, and multiply by 10000.
# table2
joined_tbl2 <- cases_tbl2 %>%
inner_join(population_tbl2, by = c("country", "year")) %>%
mutate(rate_per_10000 = (cases / population) * 10000)
# table3
computed_tbl3 <- parsed_tbl3 %>%
mutate(rate_per_10000 = (cases / population) * 10000)d. Store back in the appropriate place.
# table2
rate_table2 <- joined_tbl2
rate_table2# A tibble: 6 × 5
country year cases population rate_per_10000
<chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1999 745 19987071 0.373
2 Afghanistan 2000 2666 20595360 1.29
3 Brazil 1999 37737 172006362 2.19
4 Brazil 2000 80488 174504898 4.61
5 China 1999 212258 1272915272 1.67
6 China 2000 213766 1280428583 1.67 # table3
rate_table3 <- computed_tbl3
rate_table3# A tibble: 6 × 5
country year cases population rate_per_10000
<chr> <dbl> <int> <int> <dbl>
1 Afghanistan 1999 745 19987071 0.373
2 Afghanistan 2000 2666 20595360 1.29
3 Brazil 1999 37737 172006362 2.19
4 Brazil 2000 80488 174504898 4.61
5 China 1999 212258 1272915272 1.67
6 China 2000 213766 1280428583 1.67 5.3 Lengthening data
5.3.1 Data in column names
billboard# A tibble: 317 × 79
artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
<chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA NA
3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55 59
5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36 49
6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA NA
8 Aaliyah I Do… 2000-01-29 84 62 51 41 38 35 35 38
9 Aaliyah Try … 2000-03-18 59 53 38 28 21 18 16 14
10 Adams, Yo… Open… 2000-08-26 76 76 74 69 68 67 61 58
# ℹ 307 more rows
# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
# wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,
# wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
# wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,
# wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,
# wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank"
)# A tibble: 24,092 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk8 NA
9 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk9 NA
10 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk10 NA
# ℹ 24,082 more rowsbillboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
)# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <chr> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 wk2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 wk3 92
# ℹ 5,297 more rowsbillboard_longer <- billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
values_to = "rank",
values_drop_na = TRUE
) |>
mutate(
week = parse_number(week)
)
billboard_longer# A tibble: 5,307 × 5
artist track date.entered week rank
<chr> <chr> <date> <dbl> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
# ℹ 5,297 more rowsbillboard_longer |>
ggplot(aes(x = week, y = rank, group = track)) +
geom_line(alpha = 0.25) +
scale_y_reverse()
5.3.2 How does pivoting work?
df <- tribble(
~id, ~bp1, ~bp2,
"A", 100, 120,
"B", 140, 115,
"C", 120, 125
)df |>
pivot_longer(
cols = bp1:bp2,
names_to = "measurement",
values_to = "value"
)# A tibble: 6 × 3
id measurement value
<chr> <chr> <dbl>
1 A bp1 100
2 A bp2 120
3 B bp1 140
4 B bp2 115
5 C bp1 120
6 C bp2 1255.3.3 Many variables in column names
who2# A tibble: 7,240 × 58
country year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan 1980 NA NA NA NA NA NA
2 Afghanistan 1981 NA NA NA NA NA NA
3 Afghanistan 1982 NA NA NA NA NA NA
4 Afghanistan 1983 NA NA NA NA NA NA
5 Afghanistan 1984 NA NA NA NA NA NA
6 Afghanistan 1985 NA NA NA NA NA NA
7 Afghanistan 1986 NA NA NA NA NA NA
8 Afghanistan 1987 NA NA NA NA NA NA
9 Afghanistan 1988 NA NA NA NA NA NA
10 Afghanistan 1989 NA NA NA NA NA NA
# ℹ 7,230 more rows
# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,
# sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,
# sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,
# sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,
# sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,
# sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …who2 |>
pivot_longer(
cols = !(country:year),
names_to = c("diagnosis", "gender", "age"),
names_sep = "_",
values_to = "count"
)# A tibble: 405,440 × 6
country year diagnosis gender age count
<chr> <dbl> <chr> <chr> <chr> <dbl>
1 Afghanistan 1980 sp m 014 NA
2 Afghanistan 1980 sp m 1524 NA
3 Afghanistan 1980 sp m 2534 NA
4 Afghanistan 1980 sp m 3544 NA
5 Afghanistan 1980 sp m 4554 NA
6 Afghanistan 1980 sp m 5564 NA
7 Afghanistan 1980 sp m 65 NA
8 Afghanistan 1980 sp f 014 NA
9 Afghanistan 1980 sp f 1524 NA
10 Afghanistan 1980 sp f 2534 NA
# ℹ 405,430 more rows5.3.4 Data and variable names in the column headers
household# A tibble: 5 × 5
family dob_child1 dob_child2 name_child1 name_child2
<int> <date> <date> <chr> <chr>
1 1 1998-11-26 2000-01-29 Susan Jose
2 2 1996-06-22 NA Mark <NA>
3 3 2002-07-11 2004-04-05 Sam Seth
4 4 2004-10-10 2009-08-27 Craig Khai
5 5 2000-12-05 2005-02-28 Parker Gracie household |>
pivot_longer(
cols = !family,
names_to = c(".value", "child"),
names_sep = "_",
values_drop_na = TRUE
)# A tibble: 9 × 4
family child dob name
<int> <chr> <date> <chr>
1 1 child1 1998-11-26 Susan
2 1 child2 2000-01-29 Jose
3 2 child1 1996-06-22 Mark
4 3 child1 2002-07-11 Sam
5 3 child2 2004-04-05 Seth
6 4 child1 2004-10-10 Craig
7 4 child2 2009-08-27 Khai
8 5 child1 2000-12-05 Parker
9 5 child2 2005-02-28 Gracie5.4 Widening data
cms_patient_experience# A tibble: 500 × 5
org_pac_id org_nm measure_cd measure_title prf_rate
<chr> <chr> <chr> <chr> <dbl>
1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 63
2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 87
3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 86
4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 57
5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 85
6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP… CAHPS for MI… 24
7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 59
8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 85
9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 83
10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI… 63
# ℹ 490 more rowscms_patient_experience |>
distinct(measure_cd, measure_title)# A tibble: 6 × 2
measure_cd measure_title
<chr> <chr>
1 CAHPS_GRP_1 CAHPS for MIPS SSM: Getting Timely Care, Appointments, and Infor…
2 CAHPS_GRP_2 CAHPS for MIPS SSM: How Well Providers Communicate
3 CAHPS_GRP_3 CAHPS for MIPS SSM: Patient's Rating of Provider
4 CAHPS_GRP_5 CAHPS for MIPS SSM: Health Promotion and Education
5 CAHPS_GRP_8 CAHPS for MIPS SSM: Courteous and Helpful Office Staff
6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources cms_patient_experience |>
pivot_wider(
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 500 × 9
org_pac_id org_nm measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 0446157747 USC CARE MEDICA… CAHPS for MI… 63 NA NA
2 0446157747 USC CARE MEDICA… CAHPS for MI… NA 87 NA
3 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA 86
4 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
5 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
6 0446157747 USC CARE MEDICA… CAHPS for MI… NA NA NA
7 0446162697 ASSOCIATION OF … CAHPS for MI… 59 NA NA
8 0446162697 ASSOCIATION OF … CAHPS for MI… NA 85 NA
9 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA 83
10 0446162697 ASSOCIATION OF … CAHPS for MI… NA NA NA
# ℹ 490 more rows
# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>cms_patient_experience |>
pivot_wider(
id_cols = starts_with("org"),
names_from = measure_cd,
values_from = prf_rate
)# A tibble: 95 × 8
org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0446157747 USC C… 63 87 86 57 85
2 0446162697 ASSOC… 59 85 83 63 88
3 0547164295 BEAVE… 49 NA 75 44 73
4 0749333730 CAPE … 67 84 85 65 82
5 0840104360 ALLIA… 66 87 87 64 87
6 0840109864 REX H… 73 87 84 67 91
7 0840513552 SCL H… 58 83 76 58 78
8 0941545784 GRITM… 46 86 81 54 NA
9 1052612785 COMMU… 65 84 80 58 87
10 1254237779 OUR L… 61 NA NA 65 NA
# ℹ 85 more rows
# ℹ 1 more variable: CAHPS_GRP_12 <dbl>5.4.1 How does pivot_wider() work?
df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"B", "bp1", 140,
"B", "bp2", 115,
"A", "bp2", 120,
"A", "bp3", 105
)df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 4
id bp1 bp2 bp3
<chr> <dbl> <dbl> <dbl>
1 A 100 120 105
2 B 140 115 NAdf |>
distinct(measurement) |>
pull()[1] "bp1" "bp2" "bp3"df |>
select(-measurement, -value) |>
distinct()# A tibble: 2 × 1
id
<chr>
1 A
2 B df |>
select(-measurement, -value) |>
distinct() |>
mutate(x = NA, y = NA, z = NA)# A tibble: 2 × 4
id x y z
<chr> <lgl> <lgl> <lgl>
1 A NA NA NA
2 B NA NA NA df <- tribble(
~id, ~measurement, ~value,
"A", "bp1", 100,
"A", "bp1", 102,
"A", "bp2", 120,
"B", "bp1", 140,
"B", "bp2", 115
)df |>
pivot_wider(
names_from = measurement,
values_from = value
)# A tibble: 2 × 3
id bp1 bp2
<chr> <list> <list>
1 A <dbl [2]> <dbl [1]>
2 B <dbl [1]> <dbl [1]>df |>
group_by(id, measurement) |>
summarize(n = n(), .groups = "drop") |>
filter(n > 1)# A tibble: 1 × 3
id measurement n
<chr> <chr> <int>
1 A bp1 2Exercise 2 Instead of making a graph of 5 countries on the same graph as in the above example, use facet_wrap with scales=“free_y”.
time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed), .groups = "drop") |>
filter(Country_Region %in% c("China","France","Italy", "Korea, South", "US")) |>
ggplot(aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
facet_wrap(~ Country_Region, scales = "free_y") +
ggtitle("COVID-19 Confirmed Cases by Country")
Exercise 3 Using the daily count of confirmed cases, make a single graph with 5 countries of your choosing.
time_series_confirmed_long_daily <- time_series_confirmed_long |>
group_by(Country_Region, Date) |>
summarise(Confirmed = sum(Confirmed)) |>
mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))
time_series_confirmed_long_daily |>
filter(Country_Region %in% c("China","France","Italy", "Korea, South", "US")) |>
ggplot(aes(x = Date, y = Daily, color = Country_Region)) +
geom_smooth(method = "gam", se = FALSE) +
ggtitle("COVID-19 Confirmed Cases")
Exercise 4 Plot the cumulative deaths in the US, Canada and Mexico (you will need to download time_series_covid19_deaths_global.csv)
# 1. Read data
deaths <- read_csv("data/time_series_covid19_deaths_global.csv")
# 2. Reshape to long
deaths_long <- deaths %>%
pivot_longer(
cols = matches("^\\d{1,2}/\\d{1,2}/\\d{2}$"),
names_to = "Date",
values_to = "Deaths"
)
# 3. Parse dates
deaths_long <- deaths_long %>%
mutate(Date = mdy(Date))
# 4. Filter countries
north_america <- deaths_long %>%
filter(`Country/Region` %in% c("US", "Canada", "Mexico"))
# 5. Aggregate to national totals
na_totals <- north_america %>%
group_by(`Country/Region`, Date) %>%
summarise(Deaths = sum(Deaths, na.rm = TRUE), .groups = "drop")
# 6. Plot
ggplot(na_totals, aes(x = Date, y = Deaths, color = `Country/Region`)) +
geom_line(size = 1) +
labs(
title = "Cumulative COVID-19 Deaths: US, Canada, Mexico",
x = "Date",
y = "Cumulative deaths",
color = "Country"
) +
theme_minimal()
Exercise 5 Make a graph with the countries of your choice using the daily deaths data
# 1. Read the deaths dataset
deaths <- read_csv("data/time_series_covid19_deaths_global.csv")
# 2. Reshape to long format
deaths_long <- deaths %>%
pivot_longer(
cols = matches("^\\d{1,2}/\\d{1,2}/\\d{2}$"),
names_to = "Date",
values_to = "Deaths"
) %>%
mutate(Date = mdy(Date))
# 3. Aggregate to national totals
deaths_country <- deaths_long %>%
group_by(`Country/Region`, Date) %>%
summarise(Deaths = sum(Deaths), .groups = "drop")
# 4. Compute daily new deaths
deaths_daily <- deaths_country %>%
group_by(`Country/Region`) %>%
arrange(Date) %>%
mutate(Daily = Deaths - lag(Deaths, default = first(Deaths)))
# 5. Filter to chosen countries
plot_data <- deaths_daily %>%
filter(`Country/Region` %in% c("China","France","Italy", "Korea, South", "US"))
# 6. Plot daily deaths
ggplot(plot_data, aes(x = Date, y = Daily, color = `Country/Region`)) +
geom_smooth(method = "gam", se = FALSE) +
labs(
title = "Daily COVID-19 Deaths",
x = "Date",
y = "Daily Deaths",
color = "Country"
) +
theme_minimal()
Exercise 6 Make an animation of your choosing (do not use a graph with geom_smooth)
# 1. Read deaths dataset
deaths <- read_csv("data/time_series_covid19_deaths_global.csv")
# 2. Reshape to long format
deaths_long <- deaths %>%
pivot_longer(
cols = matches("^\\d{1,2}/\\d{1,2}/\\d{2}$"),
names_to = "Date",
values_to = "Deaths"
) %>%
mutate(Date = mdy(Date))
# 3. Aggregate to national totals
deaths_country <- deaths_long %>%
group_by(`Country/Region`, Date) %>%
summarise(Deaths = sum(Deaths), .groups = "drop")
# 4. Compute daily new deaths
deaths_daily <- deaths_country %>%
group_by(`Country/Region`) %>%
arrange(Date) %>%
mutate(Daily = Deaths - lag(Deaths, default = first(Deaths)))
# 5. Filter to chosen countries
plot_data <- deaths_daily %>%
filter(`Country/Region` %in% c("US", "Canada", "Mexico"))
# 6. Build animated plot
d <- ggplot(plot_data, aes(x = Date, y = Daily, color = `Country/Region`)) +
geom_point() +
labs(
title = "Daily COVID-19 Deaths",
subtitle = "Date: {frame_along}",
x = "Date",
y = "Daily Deaths",
color = "Country"
) +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# 7. Render animation
animate(d, renderer = gifski_renderer(), end_pause = 15)